ArticleComputational intelligence and neuroscience2022
Railway Traffic Emergency Management Relying on Image Recognition Technology in the Context of Big Data.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025Pooled it
- Retracted: Railway Traffic Emergency Management Relying on Image Recognition Technology in the Context of Big Data.Computational intelligence and neuroscience · 2023Article
- Intelligent Identification of Coal Crack in CT Images Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the full popularity of China's railwayization process, it has brought about the problem of the management ability of railway traffic safety. Railway traffic safety emergency management capabilities are low. When an accident occurs, clearer data cannot be obtained in the first time to have a general understanding of the accident. Therefore, the problem of organizing rescue has always plagued relevant railway workers. This study aims to study the improvement of railway traffic emergency management based on image recognition technology in the context of big data. To this end, this study proposes image recognition technology based on deep learning, and through the relayout of the railway traffic emergency management system, so that the railway traffic problems can be dealt within time as soon as they occur, and designed an experiment to explore the ability of image recognition. The results of the experiment show that the efficiency of the improved railway traffic emergency management system has increased by 27%, and the recognition capability has increased by 64%. It can very well help current railway workers to carry out emergency management for railway traffic safety.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.